def _import_meta_graph_def(meta_graph_def):
"""Recreates a Graph saved in a a `MetaGraphDef` proto.
This function adds all the nodes from the meta graph def proto to the current
graph, recreates all the collections, and returns a saver from saver_def.
Args:
meta_graph_def: `MetaGraphDef` protocol buffer.
Returns:
A saver constructed rom `saver_def` in `meta_graph_def`.
"""
# Gathers the list of nodes we are interested in.
importer.import_graph_def(meta_graph_def.graph_def, name="")
# Restores all the other collections.
for key, col_def in meta_graph_def.collection_def.items():
kind = col_def.WhichOneof("kind")
if kind is None:
logging.error("Cannot identify data type for collection %s. Skipping."
% key)
continue
from_proto = ops.get_from_proto_function(key)
if from_proto:
assert kind == "bytes_list"
proto_type = ops.get_collection_proto_type(key)
for value in col_def.bytes_list.value:
proto = proto_type()
proto.ParseFromString(value)
ops.add_to_collection(key, from_proto(proto))
else:
field = getattr(col_def, kind)
if kind == "node_list":
for value in field.value:
col_op = ops.get_default_graph().as_graph_element(value)
ops.add_to_collection(key, col_op)
elif kind == "int64_list":
# NOTE(opensource): This force conversion is to work around the fact
# that Python2 distinguishes between int and long, while Python3 has
# only int.
for value in field.value:
ops.add_to_collection(key, int(value))
else:
for value in field.value:
ops.add_to_collection(key, value)
if meta_graph_def.HasField("saver_def"):
return Saver(saver_def=meta_graph_def.saver_def)
else:
return Saver()
def _handle_collection_def(multi_gpu_meta_graph_def, op_names_to_replicate,
num_replicas):
allow_bytes_list_keys = [tf.GraphKeys.QUEUE_RUNNERS,
tf.GraphKeys.GLOBAL_VARIABLES,
tf.GraphKeys.TRAINABLE_VARIABLES,
tf.GraphKeys.MOVING_AVERAGE_VARIABLES,
tf.GraphKeys.LOCAL_VARIABLES,
tf.GraphKeys.MODEL_VARIABLES,
tf.GraphKeys.GRADIENTS_INFO,
tf.GraphKeys.GLOBAL_STEP]
keys_to_remove = []
for key, col_def in multi_gpu_meta_graph_def.collection_def.items():
kind = col_def.WhichOneof("kind")
# Update node_list collections (e.g., GLOBAL_STEP, TRAIN_OP, UPDATE_OP,
# LOSSES, ...)
if kind == 'node_list':
new_col_def = get_new_col_def_of_node_list(
col_def, op_names_to_replicate, num_replicas)
multi_gpu_meta_graph_def.collection_def[key].Clear()
multi_gpu_meta_graph_def.collection_def[key].CopyFrom(new_col_def)
elif kind == 'bytes_list':
if ops.get_from_proto_function(key):
# Collections in allow_bytes_list_keys will be handled
# explicitly below
# (e.g., QUEUE_RUNNERS, LOCAL_VARIABLES, ...)
if key in allow_bytes_list_keys:
continue
# Remove unhandled collections (e.g., COND_CONTEXT)
# TODO: Handle all protos in tf.GraphKeys
else:
keys_to_remove.append(key)
# Keep collections without proto function
# (e.g., user defined string)
else:
continue
else:
raise RuntimeError("Should not reach here")
for key in keys_to_remove:
del multi_gpu_meta_graph_def.collection_def[key]
# Update QUEUE_RUNNERS and LOCAL_VARIABLES collection
update_queue_runners(multi_gpu_meta_graph_def, op_names_to_replicate,
num_replicas)
update_local_variables(multi_gpu_meta_graph_def, op_names_to_replicate,
num_replicas)
update_shard_info_for_in_graph(multi_gpu_meta_graph_def, num_replicas)
def _restore_collections(dest_graph, src_meta_graph_def,
collection_keys):
"""Restores collections that we need to keep."""
scope = ""
for key in collection_keys:
collection_def = src_meta_graph_def.collection_def[key]
kind = collection_def.WhichOneof("kind")
if kind is None:
tf_logging.error(
"Cannot identify data type for collection %s. Skipping.",
key)
continue
from_proto = ops.get_from_proto_function(key)
if from_proto and kind == "bytes_list":
proto_type = ops.get_collection_proto_type(key)
# It is assumed that there are no Variables Keys in collections
for value in collection_def.bytes_list.value:
proto = proto_type()
proto.ParseFromString(value)
try:
new_value = from_proto(proto, import_scope=scope)
except:
continue
dest_graph.add_to_collection(key, new_value)
else:
field = getattr(collection_def, kind)
if kind == "node_list":
for value in field.value:
name = ops.prepend_name_scope(value, scope)
# Since the graph has been optimized, the node may no longer
# exists
try:
col_op = dest_graph.as_graph_element(name)
except (TypeError, ValueError, KeyError) as e:
continue
dest_graph.add_to_collection(key, col_op)
elif kind == "int64_list":
# NOTE(opensource): This force conversion is to work around the
# fact that Python2 distinguishes between int and long, while
# Python3 has only int.
for value in field.value:
dest_graph.add_to_collection(key, int(value))
else:
for value in field.value:
dest_graph.add_to_collection(
key, ops.prepend_name_scope(value, scope))
def _restore_collections(dest_graph, src_meta_graph_def, collection_keys):
"""Restores collections that we need to keep."""
scope = ""
for key in collection_keys:
collection_def = src_meta_graph_def.collection_def[key]
kind = collection_def.WhichOneof("kind")
if kind is None:
tf_logging.error(
"Cannot identify data type for collection %s. Skipping.", key)
continue
from_proto = ops.get_from_proto_function(key)
if from_proto and kind == "bytes_list":
proto_type = ops.get_collection_proto_type(key)
# It is assumed that there are no Variables Keys in collections
for value in collection_def.bytes_list.value:
proto = proto_type()
proto.ParseFromString(value)
try:
new_value = from_proto(proto, import_scope=scope)
except:
continue
dest_graph.add_to_collection(key, new_value)
else:
field = getattr(collection_def, kind)
if kind == "node_list":
for value in field.value:
name = ops.prepend_name_scope(value, scope)
# Since the graph has been optimized, the node may no longer
# exists
try:
col_op = dest_graph.as_graph_element(name)
except (TypeError, ValueError, KeyError) as e:
continue
dest_graph.add_to_collection(key, col_op)
elif kind == "int64_list":
# NOTE(opensource): This force conversion is to work around the
# fact that Python2 distinguishes between int and long, while
# Python3 has only int.
for value in field.value:
dest_graph.add_to_collection(key, int(value))
else:
for value in field.value:
dest_graph.add_to_collection(key,
ops.prepend_name_scope(value, scope))
def import_scoped_meta_graph_with_return_elements(
meta_graph_or_file,
clear_devices=False,
graph=None,
import_scope=None,
input_map=None,
unbound_inputs_col_name="unbound_inputs",
restore_collections_predicate=(lambda key: True),
return_elements=None):
"""Imports graph from `MetaGraphDef` and returns vars and return elements.
This function takes a `MetaGraphDef` protocol buffer as input. If
the argument is a file containing a `MetaGraphDef` protocol buffer ,
it constructs a protocol buffer from the file content. The function
then adds all the nodes from the `graph_def` field to the
current graph, recreates the desired collections, and returns a dictionary of
all the Variables imported into the name scope.
In combination with `export_scoped_meta_graph()`, this function can be used to
* Serialize a graph along with other Python objects such as `QueueRunner`,
`Variable` into a `MetaGraphDef`.
* Restart training from a saved graph and checkpoints.
* Run inference from a saved graph and checkpoints.
Args:
meta_graph_or_file: `MetaGraphDef` protocol buffer or filename (including
the path) containing a `MetaGraphDef`.
clear_devices: Boolean which controls whether to clear device information
from graph_def. Default false.
graph: The `Graph` to import into. If `None`, use the default graph.
import_scope: Optional `string`. Name scope into which to import the
subgraph. If `None`, the graph is imported to the root name scope.
input_map: A dictionary mapping input names (as strings) in `graph_def` to
`Tensor` objects. The values of the named input tensors in the imported
graph will be re-mapped to the respective `Tensor` values.
unbound_inputs_col_name: Collection name for looking up unbound inputs.
restore_collections_predicate: a predicate on collection names. A collection
named c (i.e whose key is c) will be restored iff
1) `restore_collections_predicate(c)` is True, and
2) `c != unbound_inputs_col_name`.
return_elements: A list of strings containing operation names in the
`MetaGraphDef` that will be returned as `Operation` objects; and/or
tensor names in `MetaGraphDef` that will be returned as `Tensor` objects.
Returns:
A tuple of (
dictionary of all the `Variables` imported into the name scope,
list of `Operation` or `Tensor` objects from the `return_elements` list).
Raises:
ValueError: If the graph_def contains unbound inputs.
"""
if context.executing_eagerly():
raise ValueError(
"Exporting/importing meta graphs is not supported when "
"eager execution is enabled.")
if isinstance(meta_graph_or_file, meta_graph_pb2.MetaGraphDef):
meta_graph_def = meta_graph_or_file
else:
meta_graph_def = read_meta_graph_file(meta_graph_or_file)
if unbound_inputs_col_name:
for key, col_def in meta_graph_def.collection_def.items():
if key == unbound_inputs_col_name:
kind = col_def.WhichOneof("kind")
field = getattr(col_def, kind)
if field.value and (not input_map or sorted(
[compat.as_str(v)
for v in field.value]) != sorted(input_map)):
raise ValueError(
"Graph contains unbound inputs: %s. Must "
"provide these inputs through input_map." % ",".join(
compat.as_str(v) for v in field.value
if not input_map or v not in input_map))
break
# Sets graph to default graph if it's not passed in.
graph = graph or ops.get_default_graph()
# Gathers the list of nodes we are interested in.
with graph.as_default():
producer_op_list = None
if meta_graph_def.meta_info_def.HasField("stripped_op_list"):
producer_op_list = meta_graph_def.meta_info_def.stripped_op_list
input_graph_def = meta_graph_def.graph_def
# Remove all the explicit device specifications for this node. This helps to
# make the graph more portable.
if clear_devices:
for node in input_graph_def.node:
node.device = ""
scope_to_prepend_to_names = graph.unique_name(import_scope or "",
mark_as_used=False)
imported_return_elements = importer.import_graph_def(
input_graph_def,
name=(import_scope or scope_to_prepend_to_names),
input_map=input_map,
producer_op_list=producer_op_list,
return_elements=return_elements)
# TensorFlow versions before 1.9 (not inclusive) exported SavedModels
# without a VariableDef.trainable field set.
tf_version = meta_graph_def.meta_info_def.tensorflow_version
if not tf_version:
variables_have_trainable = True
else:
variables_have_trainable = (packaging_version.parse(tf_version) >=
packaging_version.parse("1.9"))
# Sort collections so we see TRAINABLE_VARIABLES first and can default these
# variables to trainable if the value is not set in their VariableDef.
sorted_collections = []
if ops.GraphKeys.TRAINABLE_VARIABLES in meta_graph_def.collection_def:
sorted_collections.append((ops.GraphKeys.TRAINABLE_VARIABLES,
meta_graph_def.collection_def[
ops.GraphKeys.TRAINABLE_VARIABLES]))
for key, value in sorted(meta_graph_def.collection_def.items()):
if key != ops.GraphKeys.TRAINABLE_VARIABLES:
sorted_collections.append((key, value))
# Restores all the other collections.
variable_objects = {}
for key, col_def in sorted_collections:
# Don't add unbound_inputs to the new graph.
if key == unbound_inputs_col_name:
continue
if not restore_collections_predicate(key):
continue
kind = col_def.WhichOneof("kind")
if kind is None:
logging.error(
"Cannot identify data type for collection %s. Skipping.",
key)
continue
from_proto = ops.get_from_proto_function(key)
# Temporary change to allow the TFMA evaluator to read metric variables
# saved as a bytes list.
# TODO(kathywu): Remove this hack once cl/248406059 has been submitted.
if key == ops.GraphKeys.METRIC_VARIABLES:
# Metric variables will use the same proto functions as GLOBAL_VARIABLES
from_proto = ops.get_from_proto_function(
ops.GraphKeys.GLOBAL_VARIABLES)
if from_proto and kind == "bytes_list":
proto_type = ops.get_collection_proto_type(key)
if key in ops.GraphKeys._VARIABLE_COLLECTIONS: # pylint: disable=protected-access
for value in col_def.bytes_list.value:
variable = variable_objects.get(value, None)
if variable is None:
proto = proto_type()
proto.ParseFromString(value)
if not variables_have_trainable:
# If the VariableDef proto does not contain a "trainable"
# property because it was exported before that property was
# added, we default it to whether the variable is in the
# TRAINABLE_VARIABLES collection. We've sorted
# TRAINABLE_VARIABLES to be first, so trainable variables will
# be created from that collection.
proto.trainable = (
key == ops.GraphKeys.TRAINABLE_VARIABLES)
variable = from_proto(
proto, import_scope=scope_to_prepend_to_names)
variable_objects[value] = variable
graph.add_to_collection(key, variable)
else:
for value in col_def.bytes_list.value:
proto = proto_type()
proto.ParseFromString(value)
graph.add_to_collection(
key,
from_proto(proto,
import_scope=scope_to_prepend_to_names))
else:
field = getattr(col_def, kind)
if key in _COMPAT_COLLECTION_LIST:
logging.warning(
"The saved meta_graph is possibly from an older release:\n"
"'%s' collection should be of type 'byte_list', but instead "
"is of type '%s'.", key, kind)
if kind == "node_list":
for value in field.value:
col_op = graph.as_graph_element(
ops.prepend_name_scope(value,
scope_to_prepend_to_names))
graph.add_to_collection(key, col_op)
elif kind == "int64_list":
# NOTE(opensource): This force conversion is to work around the fact
# that Python2 distinguishes between int and long, while Python3 has
# only int.
for value in field.value:
graph.add_to_collection(key, int(value))
else:
for value in field.value:
graph.add_to_collection(
key,
ops.prepend_name_scope(value,
scope_to_prepend_to_names))
var_list = {}
variables = graph.get_collection(ops.GraphKeys.GLOBAL_VARIABLES,
scope=scope_to_prepend_to_names)
for v in variables:
var_list[ops.strip_name_scope(v.name,
scope_to_prepend_to_names)] = v
return var_list, imported_return_elements
def create_meta_graph_def(meta_info_def=None,
graph_def=None,
saver_def=None,
collection_list=None,
graph=None,
export_scope=None,
exclude_nodes=None,
clear_extraneous_savers=False,
strip_default_attrs=False):
# pylint: disable=line-too-long
"""Construct and returns a `MetaGraphDef` protocol buffer.
Args:
meta_info_def: `MetaInfoDef` protocol buffer.
graph_def: `GraphDef` protocol buffer.
saver_def: `SaverDef` protocol buffer.
collection_list: List of string keys to collect.
graph: The `Graph` to create `MetaGraphDef` out of.
export_scope: Optional `string`. Name scope to remove.
exclude_nodes: An iterable of nodes or `string` node names to omit from all
collection, or None.
clear_extraneous_savers: Remove any preexisting SaverDefs from the SAVERS
collection. Note this method does not alter the graph, so any
extraneous Save/Restore ops should have been removed already, as needed.
strip_default_attrs: Boolean. If `True`, default-valued attributes will be
removed from the NodeDefs. For a detailed guide, see
[Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).
Returns:
MetaGraphDef protocol buffer.
Raises:
TypeError: If the arguments are not of the correct proto buffer type.
"""
# pylint: enable=line-too-long
# Type check.
if graph and not isinstance(graph, ops.Graph):
raise TypeError(
f"graph must be of type Graph. Received type: {type(graph)}.")
if meta_info_def and not isinstance(
meta_info_def, meta_graph_pb2.MetaGraphDef.MetaInfoDef):
raise TypeError("meta_info_def must be of type MetaInfoDef. "
f"Received type: {type(meta_info_def)}.")
if graph_def and not isinstance(graph_def, graph_pb2.GraphDef):
raise TypeError("graph_def must be of type GraphDef. "
f"Received type: {type(graph_def)}.")
if saver_def and not isinstance(saver_def, saver_pb2.SaverDef):
raise TypeError(f"saver_def must be of type SaverDef. "
f"Received type: {type(saver_def)}.")
# Sets graph to default graph if it's not passed in.
graph = graph or ops.get_default_graph()
# Creates a MetaGraphDef proto.
meta_graph_def = meta_graph_pb2.MetaGraphDef()
# Adds meta_info_def.
if not meta_info_def:
meta_info_def = meta_graph_pb2.MetaGraphDef.MetaInfoDef()
# Set the tf version strings to the current tf build.
meta_info_def.tensorflow_version = versions.__version__
meta_info_def.tensorflow_git_version = versions.__git_version__
meta_graph_def.meta_info_def.MergeFrom(meta_info_def)
# Adds graph_def or the default.
if not graph_def:
meta_graph_def.graph_def.MergeFrom(graph.as_graph_def(add_shapes=True))
else:
meta_graph_def.graph_def.MergeFrom(graph_def)
# Fills in meta_info_def.stripped_op_list using the ops from graph_def.
# pylint: disable=g-explicit-length-test
if len(meta_graph_def.meta_info_def.stripped_op_list.op) == 0:
meta_graph_def.meta_info_def.stripped_op_list.MergeFrom(
stripped_op_list_for_graph(meta_graph_def.graph_def))
# pylint: enable=g-explicit-length-test
# Strip default valued attributes in graph_def.
if strip_default_attrs:
strip_graph_default_valued_attrs(meta_graph_def)
# Adds saver_def.
if saver_def:
meta_graph_def.saver_def.MergeFrom(saver_def)
# Adds collection_list.
if collection_list is not None:
clist = collection_list
else:
clist = graph.get_all_collection_keys()
for ctype in clist:
if clear_extraneous_savers and ctype == ops.GraphKeys.SAVERS:
# Avoid importing Saver here
from_proto = ops.get_from_proto_function(ctype)
add_collection_def(meta_graph_def,
ctype,
graph=graph,
export_scope=export_scope,
exclude_nodes=exclude_nodes,
override_contents=[from_proto(saver_def)])
else:
add_collection_def(meta_graph_def,
ctype,
graph=graph,
export_scope=export_scope,
exclude_nodes=exclude_nodes)
return meta_graph_def
def import_scoped_meta_graph(meta_graph_or_file,
clear_devices=False,
graph=None,
import_scope=None,
input_map=None,
unbound_inputs_col_name="unbound_inputs",
restore_collections_predicate=(lambda key: True)):
"""Recreates a `Graph` saved in a `MetaGraphDef` proto.
This function takes a `MetaGraphDef` protocol buffer as input. If
the argument is a file containing a `MetaGraphDef` protocol buffer ,
it constructs a protocol buffer from the file content. The function
then adds all the nodes from the `graph_def` field to the
current graph, recreates the desired collections, and returns a dictionary of
all the Variables imported into the name scope.
In combination with `export_scoped_meta_graph()`, this function can be used to
* Serialize a graph along with other Python objects such as `QueueRunner`,
`Variable` into a `MetaGraphDef`.
* Restart training from a saved graph and checkpoints.
* Run inference from a saved graph and checkpoints.
Args:
meta_graph_or_file: `MetaGraphDef` protocol buffer or filename (including
the path) containing a `MetaGraphDef`.
clear_devices: Boolean which controls whether to clear device information
from graph_def. Default false.
graph: The `Graph` to import into. If `None`, use the default graph.
import_scope: Optional `string`. Name scope into which to import the
subgraph. If `None`, the graph is imported to the root name scope.
input_map: A dictionary mapping input names (as strings) in `graph_def` to
`Tensor` objects. The values of the named input tensors in the imported
graph will be re-mapped to the respective `Tensor` values.
unbound_inputs_col_name: Collection name for looking up unbound inputs.
restore_collections_predicate: a predicate on collection names. A collection
named c (i.e whose key is c) will be restored iff
1) `restore_collections_predicate(c)` is True, and
2) `c != unbound_inputs_col_name`.
Returns:
A dictionary of all the `Variables` imported into the name scope.
Raises:
ValueError: If the graph_def contains unbound inputs.
"""
if context.executing_eagerly():
raise ValueError("Exporting/importing meta graphs is not supported when "
"eager execution is enabled.")
if isinstance(meta_graph_or_file, meta_graph_pb2.MetaGraphDef):
meta_graph_def = meta_graph_or_file
else:
meta_graph_def = read_meta_graph_file(meta_graph_or_file)
if unbound_inputs_col_name:
for key, col_def in meta_graph_def.collection_def.items():
if key == unbound_inputs_col_name:
kind = col_def.WhichOneof("kind")
field = getattr(col_def, kind)
if field.value and (
not input_map or
sorted([compat.as_str(v) for v in field.value]) !=
sorted(input_map)):
raise ValueError("Graph contains unbound inputs: %s. Must "
"provide these inputs through input_map." %
",".join([compat.as_str(v) for v in field.value
if not input_map or v not in input_map]))
break
# Sets graph to default graph if it's not passed in.
graph = graph or ops.get_default_graph()
# Gathers the list of nodes we are interested in.
with graph.as_default():
producer_op_list = None
if meta_graph_def.meta_info_def.HasField("stripped_op_list"):
producer_op_list = meta_graph_def.meta_info_def.stripped_op_list
input_graph_def = meta_graph_def.graph_def
# Remove all the explicit device specifications for this node. This helps to
# make the graph more portable.
if clear_devices:
for node in input_graph_def.node:
node.device = ""
scope_to_prepend_to_names = graph.unique_name(
import_scope or "", mark_as_used=False)
importer.import_graph_def(
input_graph_def,
name=(import_scope or scope_to_prepend_to_names),
input_map=input_map,
producer_op_list=producer_op_list)
# Restores all the other collections.
variable_objects = {}
for key, col_def in sorted(meta_graph_def.collection_def.items()):
# Don't add unbound_inputs to the new graph.
if key == unbound_inputs_col_name:
continue
if not restore_collections_predicate(key):
continue
kind = col_def.WhichOneof("kind")
if kind is None:
logging.error("Cannot identify data type for collection %s. Skipping.",
key)
continue
from_proto = ops.get_from_proto_function(key)
if from_proto and kind == "bytes_list":
proto_type = ops.get_collection_proto_type(key)
if key in ops.GraphKeys._VARIABLE_COLLECTIONS: # pylint: disable=protected-access
for value in col_def.bytes_list.value:
variable = variable_objects.get(value, None)
if variable is None:
proto = proto_type()
proto.ParseFromString(value)
variable = from_proto(
proto, import_scope=scope_to_prepend_to_names)
variable_objects[value] = variable
graph.add_to_collection(key, variable)
else:
for value in col_def.bytes_list.value:
proto = proto_type()
proto.ParseFromString(value)
graph.add_to_collection(
key, from_proto(
proto, import_scope=scope_to_prepend_to_names))
else:
field = getattr(col_def, kind)
if key in _COMPAT_COLLECTION_LIST:
logging.warning(
"The saved meta_graph is possibly from an older release:\n"
"'%s' collection should be of type 'byte_list', but instead "
"is of type '%s'.", key, kind)
if kind == "node_list":
for value in field.value:
col_op = graph.as_graph_element(
ops.prepend_name_scope(value, scope_to_prepend_to_names))
graph.add_to_collection(key, col_op)
elif kind == "int64_list":
# NOTE(opensource): This force conversion is to work around the fact
# that Python2 distinguishes between int and long, while Python3 has
# only int.
for value in field.value:
graph.add_to_collection(key, int(value))
else:
for value in field.value:
graph.add_to_collection(
key, ops.prepend_name_scope(value, scope_to_prepend_to_names))
var_list = {}
variables = graph.get_collection(ops.GraphKeys.GLOBAL_VARIABLES,
scope=scope_to_prepend_to_names)
for v in variables:
var_list[ops.strip_name_scope(v.name, scope_to_prepend_to_names)] = v
return var_list
def create_meta_graph_def(meta_info_def=None,
graph_def=None,
saver_def=None,
collection_list=None,
graph=None,
export_scope=None,
exclude_nodes=None,
clear_extraneous_savers=False,
strip_default_attrs=False):
# pylint: disable=line-too-long
"""Construct and returns a `MetaGraphDef` protocol buffer.
Args:
meta_info_def: `MetaInfoDef` protocol buffer.
graph_def: `GraphDef` protocol buffer.
saver_def: `SaverDef` protocol buffer.
collection_list: List of string keys to collect.
graph: The `Graph` to create `MetaGraphDef` out of.
export_scope: Optional `string`. Name scope to remove.
exclude_nodes: An iterable of nodes or `string` node names to omit from all
collection, or None.
clear_extraneous_savers: Remove any preexisting SaverDefs from the SAVERS
collection. Note this method does not alter the graph, so any
extraneous Save/Restore ops should have been removed already, as needed.
strip_default_attrs: Boolean. If `True`, default-valued attributes will be
removed from the NodeDefs. For a detailed guide, see
[Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).
Returns:
MetaGraphDef protocol buffer.
Raises:
TypeError: If the arguments are not of the correct proto buffer type.
"""
# pylint: enable=line-too-long
# Type check.
if graph and not isinstance(graph, ops.Graph):
raise TypeError("graph must be of type Graph, not %s", type(graph))
if meta_info_def and not isinstance(meta_info_def,
meta_graph_pb2.MetaGraphDef.MetaInfoDef):
raise TypeError("meta_info_def must be of type MetaInfoDef, not %s",
type(meta_info_def))
if graph_def and not isinstance(graph_def, graph_pb2.GraphDef):
raise TypeError("graph_def must be of type GraphDef, not %s",
type(graph_def))
if saver_def and not isinstance(saver_def, saver_pb2.SaverDef):
raise TypeError("saver_def must be of type SaverDef, not %s",
type(saver_def))
# Sets graph to default graph if it's not passed in.
graph = graph or ops.get_default_graph()
# Creates a MetaGraphDef proto.
meta_graph_def = meta_graph_pb2.MetaGraphDef()
# Adds meta_info_def.
if not meta_info_def:
meta_info_def = meta_graph_pb2.MetaGraphDef.MetaInfoDef()
# Set the tf version strings to the current tf build.
meta_info_def.tensorflow_version = versions.__version__
meta_info_def.tensorflow_git_version = versions.__git_version__
meta_graph_def.meta_info_def.MergeFrom(meta_info_def)
# Adds graph_def or the default.
if not graph_def:
meta_graph_def.graph_def.MergeFrom(graph.as_graph_def(add_shapes=True))
else:
meta_graph_def.graph_def.MergeFrom(graph_def)
# Fills in meta_info_def.stripped_op_list using the ops from graph_def.
# pylint: disable=g-explicit-length-test
if len(meta_graph_def.meta_info_def.stripped_op_list.op) == 0:
meta_graph_def.meta_info_def.stripped_op_list.MergeFrom(
stripped_op_list_for_graph(meta_graph_def.graph_def))
# pylint: enable=g-explicit-length-test
# Strip default valued attributes in graph_def.
if strip_default_attrs:
_strip_graph_default_valued_attrs(meta_graph_def)
# Adds saver_def.
if saver_def:
meta_graph_def.saver_def.MergeFrom(saver_def)
# Adds collection_list.
if collection_list is not None:
clist = collection_list
else:
clist = graph.get_all_collection_keys()
for ctype in clist:
if clear_extraneous_savers and ctype == ops.GraphKeys.SAVERS:
# Avoid importing Saver here
from_proto = ops.get_from_proto_function(ctype)
add_collection_def(meta_graph_def, ctype,
graph=graph,
export_scope=export_scope,
exclude_nodes=exclude_nodes,
override_contents=[from_proto(saver_def)])
else:
add_collection_def(meta_graph_def, ctype,
graph=graph,
export_scope=export_scope,
exclude_nodes=exclude_nodes)
return meta_graph_def
def import_scoped_meta_graph(meta_graph_or_file,
clear_devices=False,
graph=None,
import_scope=None,
input_map=None,
unbound_inputs_col_name="unbound_inputs",
restore_collections_predicate=(lambda key: True)):
"""Recreates a `Graph` saved in a `MetaGraphDef` proto.
This function takes a `MetaGraphDef` protocol buffer as input. If
the argument is a file containing a `MetaGraphDef` protocol buffer ,
it constructs a protocol buffer from the file content. The function
then adds all the nodes from the `graph_def` field to the
current graph, recreates the desired collections, and returns a dictionary of
all the Variables imported into the name scope.
In combination with `export_scoped_meta_graph()`, this function can be used to
* Serialize a graph along with other Python objects such as `QueueRunner`,
`Variable` into a `MetaGraphDef`.
* Restart training from a saved graph and checkpoints.
* Run inference from a saved graph and checkpoints.
Args:
meta_graph_or_file: `MetaGraphDef` protocol buffer or filename (including
the path) containing a `MetaGraphDef`.
clear_devices: Boolean which controls whether to clear device information
from graph_def. Default false.
graph: The `Graph` to import into. If `None`, use the default graph.
import_scope: Optional `string`. Name scope into which to import the
subgraph. If `None`, the graph is imported to the root name scope.
input_map: A dictionary mapping input names (as strings) in `graph_def` to
`Tensor` objects. The values of the named input tensors in the imported
graph will be re-mapped to the respective `Tensor` values.
unbound_inputs_col_name: Collection name for looking up unbound inputs.
restore_collections_predicate: a predicate on collection names. A collection
named c (i.e whose key is c) will be restored iff
1) `restore_collections_predicate(c)` is True, and
2) `c != unbound_inputs_col_name`.
Returns:
A dictionary of all the `Variables` imported into the name scope.
Raises:
ValueError: If the graph_def contains unbound inputs.
"""
if context.in_eager_mode():
raise ValueError("Exporting/importing meta graphs is not supported when "
"eager execution is enabled.")
if isinstance(meta_graph_or_file, meta_graph_pb2.MetaGraphDef):
meta_graph_def = meta_graph_or_file
else:
meta_graph_def = read_meta_graph_file(meta_graph_or_file)
if unbound_inputs_col_name:
for key, col_def in meta_graph_def.collection_def.items():
if key == unbound_inputs_col_name:
kind = col_def.WhichOneof("kind")
field = getattr(col_def, kind)
if field.value and (
not input_map or
sorted([compat.as_str(v) for v in field.value]) !=
sorted(input_map)):
raise ValueError("Graph contains unbound inputs: %s. Must "
"provide these inputs through input_map." %
",".join([compat.as_str(v) for v in field.value
if not input_map or v not in input_map]))
break
# Sets graph to default graph if it's not passed in.
graph = graph or ops.get_default_graph()
# Gathers the list of nodes we are interested in.
with graph.as_default():
producer_op_list = None
if meta_graph_def.meta_info_def.HasField("stripped_op_list"):
producer_op_list = meta_graph_def.meta_info_def.stripped_op_list
input_graph_def = meta_graph_def.graph_def
# Remove all the explicit device specifications for this node. This helps to
# make the graph more portable.
if clear_devices:
for node in input_graph_def.node:
node.device = ""
scope_to_prepend_to_names = graph.unique_name(
import_scope or "", mark_as_used=False)
importer.import_graph_def(
input_graph_def, name=(import_scope or ""), input_map=input_map,
producer_op_list=producer_op_list)
# Restores all the other collections.
for key, col_def in sorted(meta_graph_def.collection_def.items()):
# Don't add unbound_inputs to the new graph.
if key == unbound_inputs_col_name:
continue
if not restore_collections_predicate(key):
continue
kind = col_def.WhichOneof("kind")
if kind is None:
logging.error("Cannot identify data type for collection %s. Skipping.",
key)
continue
from_proto = ops.get_from_proto_function(key)
if from_proto and kind == "bytes_list":
proto_type = ops.get_collection_proto_type(key)
for value in col_def.bytes_list.value:
proto = proto_type()
proto.ParseFromString(value)
graph.add_to_collection(
key, from_proto(proto, import_scope=scope_to_prepend_to_names))
else:
field = getattr(col_def, kind)
if key in _COMPAT_COLLECTION_LIST:
logging.warning(
"The saved meta_graph is possibly from an older release:\n"
"'%s' collection should be of type 'byte_list', but instead "
"is of type '%s'.", key, kind)
if kind == "node_list":
for value in field.value:
col_op = graph.as_graph_element(
ops.prepend_name_scope(value, scope_to_prepend_to_names))
graph.add_to_collection(key, col_op)
elif kind == "int64_list":
# NOTE (opensource): This force conversion is to work around the fact id:3223 gh:3224
# that Python2 distinguishes between int and long, while Python3 has
# only int.
for value in field.value:
graph.add_to_collection(key, int(value))
else:
for value in field.value:
graph.add_to_collection(
key, ops.prepend_name_scope(value, scope_to_prepend_to_names))
var_list = {}
variables = graph.get_collection(ops.GraphKeys.GLOBAL_VARIABLES,
scope=scope_to_prepend_to_names)
for v in variables:
var_list[ops.strip_name_scope(v.name, scope_to_prepend_to_names)] = v
return var_list
def import_scoped_meta_graph(meta_graph_or_file,
clear_devices=False,
graph=None,
import_scope=None,
input_map=None,
unbound_inputs_col_name="unbound_inputs"):
"""Recreates a`Graph` saved in a `MetaGraphDef` proto.
This function takes a `MetaGraphDef` protocol buffer as input. If
the argument is a file containing a `MetaGraphDef` protocol buffer ,
it constructs a protocol buffer from the file content. The function
then adds all the nodes from the `graph_def` field to the
current graph, recreates all the collections, and returns a saver
constructed from the `saver_def` field.
In combination with `export_scoped_meta_graph()`, this function can be used to
* Serialize a graph along with other Python objects such as `QueueRunner`,
`Variable` into a `MetaGraphDef`.
* Restart training from a saved graph and checkpoints.
* Run inference from a saved graph and checkpoints.
Args:
meta_graph_or_file: `MetaGraphDef` protocol buffer or filename (including
the path) containing a `MetaGraphDef`.
clear_devices: Boolean which controls whether to clear device information
from graph_def. Default false.
graph: The `Graph` to import into. If `None`, use the default graph.
import_scope: Optional `string`. Name scope into which to import the
subgraph. If `None`, the graph is imported to the root name scope.
input_map: A dictionary mapping input names (as strings) in `graph_def` to
`Tensor` objects. The values of the named input tensors in the imported
graph will be re-mapped to the respective `Tensor` values.
unbound_inputs_col_name: Collection name for looking up unbound inputs.
Returns:
A dictionary of all the `Variables` imported into the name scope.
Raises:
ValueError: If the graph_def contains unbound inputs.
"""
if isinstance(meta_graph_or_file, meta_graph_pb2.MetaGraphDef):
meta_graph_def = meta_graph_or_file
else:
meta_graph_def = read_meta_graph_file(meta_graph_or_file)
if unbound_inputs_col_name:
for key, col_def in meta_graph_def.collection_def.items():
if key == unbound_inputs_col_name:
kind = col_def.WhichOneof("kind")
field = getattr(col_def, kind)
if field.value and (
not input_map or
sorted([compat.as_str(v) for v in field.value]) !=
sorted(input_map)):
raise ValueError("Graph contains unbound inputs: %s. Must "
"provide these inputs through input_map." %
",".join([compat.as_str(v) for v in field.value]))
break
# Sets graph to default graph if it's not passed in.
graph = graph or ops.get_default_graph()
# Gathers the list of nodes we are interested in.
with graph.as_default():
producer_op_list = None
if meta_graph_def.meta_info_def.HasField("stripped_op_list"):
producer_op_list = meta_graph_def.meta_info_def.stripped_op_list
input_graph_def = meta_graph_def.graph_def
# Remove all the explicit device specifications for this node. This helps to
# make the graph more portable.
if clear_devices:
for node in input_graph_def.node:
node.device = ""
importer.import_graph_def(
input_graph_def, name=(import_scope or ""), input_map=input_map,
producer_op_list=producer_op_list)
# Restores all the other collections.
for key, col_def in meta_graph_def.collection_def.items():
# Don't add unbound_inputs to the new graph.
if key == unbound_inputs_col_name:
continue
kind = col_def.WhichOneof("kind")
if kind is None:
logging.error("Cannot identify data type for collection %s. Skipping.",
key)
continue
from_proto = ops.get_from_proto_function(key)
if from_proto:
assert kind == "bytes_list"
proto_type = ops.get_collection_proto_type(key)
for value in col_def.bytes_list.value:
proto = proto_type()
proto.ParseFromString(value)
graph.add_to_collection(
key, from_proto(proto, import_scope=import_scope))
else:
field = getattr(col_def, kind)
if kind == "node_list":
for value in field.value:
col_op = graph.as_graph_element(
ops.prepend_name_scope(value, import_scope))
graph.add_to_collection(key, col_op)
elif kind == "int64_list":
# NOTE(opensource): This force conversion is to work around the fact
# that Python2 distinguishes between int and long, while Python3 has
# only int.
for value in field.value:
graph.add_to_collection(key, int(value))
else:
for value in field.value:
graph.add_to_collection(
key, ops.prepend_name_scope(value, import_scope))
var_list = {}
variables = graph.get_collection(ops.GraphKeys.VARIABLES,
scope=import_scope)
for v in variables:
var_list[ops.strip_name_scope(v.name, import_scope)] = v
return var_list
